TY - JOUR
TI - Mixture of Metrics Optimization for Machine Learning Problems
AU - Wiercioch, Magdalena
AU - Śmieja, Marek
TI - Mixture of Metrics Optimization for Machine Learning Problems
AB - The selection of data representation and metric for a given data set is one of the most crucial problems in machine learning since it affects the results of classification and clustering methods. In this paper we investigate how to combine a various data representations and metrics into a single function which better reflects the relationships between data set elements than a single representation-metric pair. Our approach relies on optimizing a linear combination of selected distance measures with use of least square approximation. The application of our method for classification and clustering of chemical compounds seems to increase the accuracy of these methods.
VL - 2015
IS - Volume 24
PY - 2016
SN - 1732-3916
C1 - 2083-8476
SP - 83
EP - 92
DO - 10.4467/20838476SI.15.008.3030
UR - https://ejournals.eu/en/journal/schedae-informaticae/article/mixture-of-metrics-optimization-for-machine-learning-problems
KW - metric learning
KW - classification
KW - clustering
KW - chemical compound activity
KW - fingerprint